{
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  "metadata": {
    "colab": {
      "name": "market_profile.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMBjnJDRN785OL7PAFaloZi",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/letianzj/QuantResearch/blob/master/market/market_profile.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M8EUhtBem9v7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "today = '20200901'               # change day of interest\n",
        "sym = 'SPY'"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mDjtJ117FaVv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "b5cd422c-dde1-4950-d4e4-56266fa8491c"
      },
      "source": [
        "!pip install yfinance"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: yfinance in /usr/local/lib/python3.6/dist-packages (0.1.54)\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.6/dist-packages (from yfinance) (1.0.5)\n",
            "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.6/dist-packages (from yfinance) (2.23.0)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.6/dist-packages (from yfinance) (1.18.5)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.6/dist-packages (from yfinance) (0.0.9)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24->yfinance) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24->yfinance) (2.8.1)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (2020.6.20)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (2.10)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.6.1->pandas>=0.24->yfinance) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VqIHwb3pnIlT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import yfinance as yf\n",
        "from datetime import datetime, timedelta\n",
        "import plotly.graph_objects as go"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Jc-HwBQnR3h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def volume_profile(df, price_pace=0.25, return_raw=False):\n",
        "    \"\"\"\n",
        "    create volume profile\n",
        "    :param df: time-indexed HOLCV bar or time-indexed P-V tick\n",
        "    :param price_pace: price bucket, default 5 cents\n",
        "    :param return_raw: return raw data or figure\n",
        "    :return: raw data or figure obj\n",
        "    \"\"\"\n",
        "\n",
        "    cmin = min(df.Close)\n",
        "    cmax = max(df.Close)\n",
        "    cmin_int = int(cmin / price_pace) * price_pace  # int(0.9) = 0\n",
        "    cmax_int = int(cmax / price_pace) * price_pace\n",
        "    if cmax_int < cmax:\n",
        "        cmax_int += price_pace\n",
        "    cmax_int += price_pace  # right bracket is not included in arrange\n",
        "\n",
        "    price_buckets = np.arange(cmin_int, cmax_int, price_pace)\n",
        "    price_coors = pd.Series(price_buckets).rolling(2).mean().dropna()\n",
        "    vol_bars = np.histogram(df.Close, bins=price_buckets, weights=df.Volume)[0]\n",
        "\n",
        "    if return_raw:\n",
        "        return (price_coors.values, vol_bars)\n",
        "\n",
        "    fig1 = go.Candlestick(\n",
        "        x=df.index,\n",
        "        open=df.Open,\n",
        "        high=df.High,\n",
        "        low=df.Low,\n",
        "        close=df.Close,\n",
        "        xaxis='x',\n",
        "        yaxis='y2',\n",
        "        visible=True,\n",
        "        showlegend=False\n",
        "    )\n",
        "\n",
        "    fig2 = go.Bar(\n",
        "        x=df.index,\n",
        "        y=df.Volume,\n",
        "        yaxis='y',\n",
        "        name='Volume',\n",
        "        showlegend=False\n",
        "    )\n",
        "\n",
        "    fig3 = go.Bar(\n",
        "        x=vol_bars,\n",
        "        y=price_coors.values,\n",
        "        orientation='h',\n",
        "        xaxis='x2',\n",
        "        yaxis='y3',\n",
        "        visible=True,\n",
        "        showlegend=False,\n",
        "        marker_color='blue',\n",
        "        opacity=0.2\n",
        "    )\n",
        "\n",
        "    low = cmin_int\n",
        "    high = cmax_int\n",
        "    layout = go.Layout(\n",
        "        title=go.layout.Title(text=\"Volume Profile\"),\n",
        "        xaxis=go.layout.XAxis(\n",
        "            side=\"bottom\",\n",
        "            title=\"Date\",\n",
        "            rangeslider=go.layout.xaxis.Rangeslider(visible=False)\n",
        "        ),\n",
        "        yaxis=go.layout.YAxis(\n",
        "            side=\"right\",\n",
        "            title='Volume',\n",
        "            showticklabels=False,\n",
        "            domain=[0, 0.2]\n",
        "        ),\n",
        "        yaxis2=go.layout.YAxis(\n",
        "            side=\"right\",\n",
        "            title='Price',\n",
        "            range=[low, high],\n",
        "            domain=[0.2, 1.0]\n",
        "        ),\n",
        "        xaxis2=go.layout.XAxis(\n",
        "            side=\"top\",\n",
        "            showgrid=False,\n",
        "            # volume bar on the right\n",
        "            # unfortunately reversed is an auto-range\n",
        "            # one solution is to add an invisible bar.\n",
        "            # https://community.plotly.com/t/reversed-axis-with-range-specified/3806\n",
        "            # autorange='reversed',\n",
        "            ticks='',\n",
        "            showticklabels=False,\n",
        "            range=[0, int(vol_bars.max() * 5)],\n",
        "            overlaying=\"x\"\n",
        "        ),\n",
        "        yaxis3=go.layout.YAxis(\n",
        "            side=\"left\",\n",
        "            range=[low, high],\n",
        "            showticklabels=False,\n",
        "            overlaying=\"y2\"\n",
        "        ),\n",
        "    )\n",
        "\n",
        "    fig = go.Figure(data=[fig1, fig2, fig3], layout=layout)\n",
        "\n",
        "    return fig"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ArZ5qlGwGOiG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def market_profile(df, price_pace=0.25, time_pace='30T', return_raw=False):\n",
        "    \"\"\"\n",
        "    create market profile\n",
        "    :param df: time-indexed HOLCV bar or time-indexed P-V tick\n",
        "    :param price_pace: price bucket, default 5 cents\n",
        "    :param time_pace: time bucket, default to 30min (30T)\n",
        "    :param return_raw: return raw data or figure\n",
        "    :return: raw data or figure obj\n",
        "    \"\"\"\n",
        "    cmin = min(df.Low)\n",
        "    cmax = max(df.High)\n",
        "    cmin_int = int(cmin / price_pace) * price_pace  # int(0.9) = 0\n",
        "    cmax_int = int(cmax / price_pace) * price_pace\n",
        "    if cmax_int < cmax:\n",
        "        cmax_int += price_pace\n",
        "    cmax_int += price_pace  # right bracket is not included in arrange\n",
        "\n",
        "    price_buckets = np.arange(cmin_int, cmax_int, 0.25)\n",
        "    price_coors = pd.Series(price_buckets).rolling(2).mean().dropna()\n",
        "    df_agg = df.resample(time_pace).agg({'High': 'max', 'Low': 'min'})\n",
        "    tpo_bars = np.zeros([price_buckets.shape[0] - 1, df_agg.shape[0]], dtype=np.int32)\n",
        "    j = 0\n",
        "    for idx, row in df_agg.iterrows():\n",
        "        time_bars = np.histogram([row.Low, row.High], bins=price_buckets)[0]\n",
        "        result = np.where(time_bars == 1)[0]\n",
        "        if result.shape[0] == 2:\n",
        "            time_bars[result[0]:result[1] + 1] = 1\n",
        "\n",
        "        tpo_bars[:, j] = time_bars\n",
        "        j += 1\n",
        "\n",
        "    if return_raw:\n",
        "        return (price_coors.values, tpo_bars)\n",
        "\n",
        "    fig = go.Figure()\n",
        "\n",
        "    fig1 = go.Candlestick(\n",
        "        x=df.index,\n",
        "        open=df.Open,\n",
        "        high=df.High,\n",
        "        low=df.Low,\n",
        "        close=df.Close,\n",
        "        xaxis='x',\n",
        "        yaxis='y2',\n",
        "        visible=True,\n",
        "        showlegend=False\n",
        "    )\n",
        "    fig.add_trace(fig1)\n",
        "\n",
        "    fig2 = go.Bar(\n",
        "        x=df.index,\n",
        "        y=df.Volume,\n",
        "        yaxis='y',\n",
        "        name='Volume',\n",
        "        showlegend=False\n",
        "    )\n",
        "    fig.add_trace(fig2)\n",
        "\n",
        "    for j in range(tpo_bars.shape[1]):\n",
        "        for i in range(tpo_bars.shape[0]):\n",
        "            fig.add_trace(go.Bar(\n",
        "                x=[tpo_bars[i, j]],\n",
        "                y=[price_coors.values[i]],\n",
        "                orientation='h',\n",
        "                xaxis='x2',\n",
        "                yaxis='y3',\n",
        "                visible=True,\n",
        "                showlegend=False,\n",
        "                opacity=0.2,\n",
        "                marker=dict(\n",
        "                    color='blue',\n",
        "                    line=dict(color='rgb(248, 248, 249)', width=1)\n",
        "                )))\n",
        "\n",
        "    low = cmin_int\n",
        "    high = cmax_int\n",
        "    fig.update_layout(\n",
        "        title=go.layout.Title(text=\"TPO Profile\"),\n",
        "        xaxis=go.layout.XAxis(\n",
        "            side=\"bottom\",\n",
        "            title=\"Date\",\n",
        "            rangeslider=go.layout.xaxis.Rangeslider(visible=False)\n",
        "        ),\n",
        "        yaxis=go.layout.YAxis(\n",
        "            side=\"right\",\n",
        "            title='Volume',\n",
        "            showticklabels=False,\n",
        "            domain=[0, 0.2]\n",
        "        ),\n",
        "        yaxis2=go.layout.YAxis(\n",
        "            side=\"right\",\n",
        "            title='Price',\n",
        "            range=[low, high],\n",
        "            domain=[0.2, 1.0]\n",
        "        ),\n",
        "        xaxis2=go.layout.XAxis(\n",
        "            side=\"top\",\n",
        "            showgrid=False,\n",
        "            ticks='',\n",
        "            showticklabels=False,\n",
        "            range=[0, max(tpo_bars.sum(axis=1))*5],\n",
        "            overlaying=\"x\"\n",
        "        ),\n",
        "        yaxis3=go.layout.YAxis(\n",
        "            side=\"left\",\n",
        "            range=[low, high],\n",
        "            showticklabels=False,\n",
        "            overlaying=\"y2\"\n",
        "        ),\n",
        "\n",
        "        barmode='stack',\n",
        "        # paper_bgcolor='rgb(248, 248, 255)',\n",
        "        # plot_bgcolor='rgb(248, 248, 255)',\n",
        "        showlegend=False,\n",
        "    )\n",
        "\n",
        "    # Annotation\n",
        "    annotations = []\n",
        "    for i in range(tpo_bars.shape[0]):  # price\n",
        "        non_zero_time = 0\n",
        "        for j in range(tpo_bars.shape[1]):        # time\n",
        "            if tpo_bars[i, j] == 0:\n",
        "                continue\n",
        "            annotations.append(dict(xref='x2', yref='y3',\n",
        "                                    x=non_zero_time+0.5, y=price_coors.values[i],\n",
        "                                    text=str(j),\n",
        "                                    font=dict(family='Arial', size=14,\n",
        "                                              color='black'),\n",
        "                                    showarrow=False))\n",
        "            non_zero_time += 1\n",
        "    fig.update_layout(annotations=annotations)\n",
        "    return fig"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oPwvGo2RnWdy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7035674a-67db-463e-a858-5011264bf2f5"
      },
      "source": [
        "sd = datetime.strptime(today, '%Y%m%d')\n",
        "ed = sd + timedelta(days=1)\n",
        "\n",
        "df = yf.download(tickers=sym, start=sd, end=ed, interval=\"1m\")\n",
        "df = df[['Open', 'High', 'Low', 'Close', 'Volume']]"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LGUco0AbGYaV",
        "colab_type": "text"
      },
      "source": [
        "## Volume Profile"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o2JxEV4TncXh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "c82489dd-879a-4b21-ca3e-1a7cb0802d37"
      },
      "source": [
        "# change price_pace to control number of bars\n",
        "fig = volume_profile(df, price_pace=0.1)\n",
        "fig.show()"
      ],
      "execution_count": 47,
      "outputs": [
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          "metadata": {
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7WU8XG4fGgtR",
        "colab_type": "text"
      },
      "source": [
        "## TPO Profile"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0C55LfcboQHz",
        "colab_type": "code",
        "colab": {
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          "height": 542
        },
        "outputId": "cfdc88bd-0820-42a9-b9bb-07d086ba717a"
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      "source": [
        "# change price_pace to control number of bars\n",
        "# change time_space to control bar time window\n",
        "# fig = market_profile(df, price_pace=0.25, time_pace='10T', return_raw=False)\n",
        "fig = market_profile(df, price_pace=0.25, time_pace='30T', return_raw=False)\n",
        "fig.show()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
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          "metadata": {
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    {
      "cell_type": "code",
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        ""
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      "execution_count": 48,
      "outputs": []
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}